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On the optimization of water-energy nexus in shale gas network under price uncertainties

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  • Oke, Doris
  • Mukherjee, Rajib
  • Sengupta, Debalina
  • Majozi, Thokozani
  • El-Halwagi, Mahmoud

Abstract

This study develops a framework for water-energy nexus optimization in shale gas production and distribution network under uncertainty. Sustainable design of the network is achieved through treatment of wastewater using thermal membrane distillation, whereby a comprehensive design model is integrated within the network to account for energy requirement of the unit. The proposed model also accounts for the problem of scheduling hydraulic fracturing using a continuous time formulation. Various uncertainties are associated with the network. Among different uncertain variables, uncertainties associated with price and demand are crucial as they can affect the optimal configuration significantly. Incorporation of uncertainty in the model seeks to meet the demand of natural gas consumers whilst accounting for the uncertainties associated with the price of final products. Uncertainties are modelled via randomly generated scenarios using beta distribution of the price generated using historical data. The stochastic model is applied to a case study through the maximization of net profit. Three different scenarios are considered for analysis. Results from the three different scenarios show 14.39%, 11.49%, and 12.34% increase in profit respectively as compared to the deterministic approach. Solving all the scenarios together gives an ensemble-average solution with 13.74% increase in expected profit. Savings in the freshwater requirement for fracturing and the energy associated with water management amounted to 23.2% and 42.7%, respectively.

Suggested Citation

  • Oke, Doris & Mukherjee, Rajib & Sengupta, Debalina & Majozi, Thokozani & El-Halwagi, Mahmoud, 2020. "On the optimization of water-energy nexus in shale gas network under price uncertainties," Energy, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s036054422030877x
    DOI: 10.1016/j.energy.2020.117770
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    References listed on IDEAS

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    1. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    2. J. David Hughes, 2013. "A reality check on the shale revolution," Nature, Nature, vol. 494(7437), pages 307-308, February.
    3. Fadhil Y. Al-Aboosi & Mahmoud M. El-Halwagi, 2019. "A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty," Sustainability, MDPI, vol. 11(18), pages 1-39, September.
    4. Jiang, Zhiqiang & Li, Rongbo & Li, Anqiang & Ji, Changming, 2018. "Runoff forecast uncertainty considered load adjustment model of cascade hydropower stations and its application," Energy, Elsevier, vol. 158(C), pages 693-708.
    5. Oke, Doris & Mukherjee, Rajib & Sengupta, Debalina & Majozi, Thokozani & El-Halwagi, Mahmoud M., 2019. "Optimization of water-energy nexus in shale gas exploration: From production to transmission," Energy, Elsevier, vol. 183(C), pages 651-669.
    6. Jorge Chebeir & Aryan Geraili & Jose Romagnoli, 2017. "Development of Shale Gas Supply Chain Network under Market Uncertainties," Energies, MDPI, vol. 10(2), pages 1-31, February.
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    3. Zhang, Yang & Xue, Qiang & Chen, Liu & Zhang, Jinbo & Xie, Yulei & Lu, Lu & Guo, Huaicheng & Qin, Changbo & Wang, Jinnan, 2025. "Toward the efficient and accurate management of the water-energy nexus: Optimization modeling based on urban resource metabolic mechanism identification," Applied Energy, Elsevier, vol. 382(C).
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